电子测量与仪器学报

李江,李春,许子非,金江涛.旋转机械状态非线性特征提取及状态分类[J].电子测量与仪器学报,2020,34(5):65-74

旋转机械状态非线性特征提取及状态分类

Nonlinear feature extraction and state classification for rotating machine

DOI：

英文关键词:variational mode decomposition nonlinearity feature extraction state classification

基金项目:贵州省高校人文社科项目(2018ZC097)资助

作者	单位
李江	1. 黔南民族职业技术学院管理学院
李春	2. 上海理工大学能源与动力工程学院
许子非	2. 上海理工大学能源与动力工程学院
金江涛	2. 上海理工大学能源与动力工程学院

Author	Institution
Li Jiang	1. School of Management, Qiannan Nationality Professional Technology College
Li Chun	2. Energy and Power Engineering Institute, University of Shanghai for Science and Technology
Xu Zifei	2. Energy and Power Engineering Institute, University of Shanghai for Science and Technology
Jin Jiangtao	2. Energy and Power Engineering Institute, University of Shanghai for Science and Technology

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中文摘要:

为提取淹没于环境和结构噪声下风力机轴承故障信号,基于能量追踪法,提出改进变分模态分解法(improved variational mode decomposition, IVMD),并采用粒子群算法求解最优约束因子,获取准确模态分量;摒弃传统对故障特征频分量的提取,基于非线性分形理论提出多重分形谱特征因子(multi-fractal spectrum,MFC)以选取最具非线性特征的模态分量,以不同故障程度及状态的轴承加速度信号为对象,采用优化递归变分模态分解获取多分量,通过多重分形谱特征因子最大值选取有效信息分量,通过支持向量机进行故障分类。结果表明优化递归变分模态分解可准确分解振动信号至不同频段,以便故障信息提取;多重分形谱特征因子与信噪比呈正相关,以其最大值选取的分量具备更多有效信息;对 IVMD-MFC 所选取非线性分量,通过 8 种非线性特征值构建特征矩阵,通过 BP 神经网络实现故障分类,诊断准确度达 97. 5%。表明所提出方法可对不同故障程度的轴承状态进行区分。

英文摘要:

In order to extract the wind turbine bearing fault signal submerged under environmental and structural noise, a recursive variational mode decomposition method is proposed based on the energy tracking method, and the particle swarm optimization algorithm is used to solve the optimal constraint factor to obtain the accurate modal component. Based on the nonlinear fractal, the theory proposes a multifractal spectral feature factor to select the best modal component. Taking the fault degree and the loaded bearing acceleration signal as the object, the optimized recursive variational mode decomposition is used to obtain multiple components. The effective information component is selected by the maximum value of the multifractal spectral feature factor, and the fault classification is performed by the support vector machine. The results show that the optimized recursive variational mode decomposition can accurately decompose the vibration signal to different frequency bands for fault information extraction; the multifractal spectrum feature factor is positively correlated with the signal to noise ratio, and the component selected by its maximum value has more effective information; The BPNN is used to classified the hybrid fault degrees of different states, the test samples are constructed by selected components by IVMD-MFC with eight nonlinear characteristics. The diagnostic accuracy is 97. 5%. There is a good performance in hybrid fault degree of different status classification.

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